521e62daf1
* remove the bos token from dpo outputs * don't forget to fix prompt_input_ids too * use processing_class instead of tokenizer * fix for processing class
86 lines
2.7 KiB
Python
86 lines
2.7 KiB
Python
"""
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E2E tests for qwen
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"""
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import logging
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import os
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from pathlib import Path
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import pytest
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import yaml
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from accelerate.test_utils import execute_subprocess_async
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from transformers.testing_utils import get_torch_dist_unique_port
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from axolotl.utils.dict import DictDefault
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LOG = logging.getLogger("axolotl.tests.qwen")
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os.environ["WANDB_DISABLED"] = "true"
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class TestE2eQwen:
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"""
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Test cases for qwen models
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"""
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@pytest.mark.parametrize("base_model", ["Qwen/Qwen2-0.5B", "Qwen/Qwen2.5-0.5B"])
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def test_dpo(self, base_model, temp_dir):
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# pylint: disable=duplicate-code
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cfg = DictDefault(
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{
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"base_model": base_model,
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"rl": "dpo",
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"chat_template": "qwen_25",
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"sequence_len": 2048,
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"val_set_size": 0.0,
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"datasets": [
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{
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"path": "fozziethebeat/alpaca_messages_2k_dpo_test",
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"split": "train",
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"type": "chat_template.default",
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"field_messages": "conversation",
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"field_chosen": "chosen",
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"field_rejected": "rejected",
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"message_field_role": "role",
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"message_field_content": "content",
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"roles": {
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"system": ["system"],
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"user": ["user"],
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"assistant": ["assistant"],
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},
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},
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],
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"num_epochs": 1,
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"max_steps": 5,
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"warmup_steps": 20,
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"micro_batch_size": 2,
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"gradient_accumulation_steps": 2,
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"output_dir": temp_dir,
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"learning_rate": 0.00001,
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"optimizer": "adamw_bnb_8bit",
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"lr_scheduler": "cosine",
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"flash_attention": True,
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"bf16": "auto",
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"tf32": True,
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"gradient_checkpointing": True,
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}
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)
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# write cfg to yaml file
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Path(temp_dir).mkdir(parents=True, exist_ok=True)
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with open(Path(temp_dir) / "config.yaml", "w", encoding="utf-8") as fout:
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fout.write(yaml.dump(cfg.to_dict(), Dumper=yaml.Dumper))
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execute_subprocess_async(
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[
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"accelerate",
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"launch",
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"--num-processes",
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"2",
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"--main_process_port",
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f"{get_torch_dist_unique_port()}",
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"-m",
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"axolotl.cli.train",
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str(Path(temp_dir) / "config.yaml"),
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]
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)
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